Surface damage occurs in turbine parts as a result of erosion, corrosion, fretting, wear or impact by foreign objects and particulates. This loss of surface integrity and component geometry leads to losses in aerodynamic and thermal efficiency, and reduced power output for a given fuel burn. More importantly, parts suffer internal structural damage, which leads to metallurgical deterioration and ultimately component failure, engine shutdown and unscheduled maintenance.
The cyclic nature of power demands and the high frequency pressure fluctuations due to turbulence in gas flows leads to internal microstructural damage in the form of fatigue, cold creep, stress corrosion, low cycle fatigue (LCF), high cycle fatigue (HCF) and corrosion fatigue in rotating as well as stationary parts such as compressor blades, vanes and discs, turbine discs, shafts and spacers. The components in the hot gas stream, such as turbine discs and blades, guide vanes, seals, and combustor casings and linings, suffer from cyclic fluctuations in temperature as well as inertia loads, both of which also cause internal microstructural damage due to high temperature creep, thermal fatigue, thermal-mechanical fatigue (TMF), creep-fatigue environment interactions and/or high temperature low cycle fatigue (HTLCF), and various combinations of these mechanisms. The net result of the combined action of all of these damage modes is that many of these high cost components have finite lives.
The challenge to manufacturers and the operators of engines is to determine when to inspect and overhaul the engine, and when to repair or replace the used parts, all of which involve downtime of equipment and high cost for manpower and replacement parts. Failure to deal adequately with any of these challenges may lead to unexpected failures, unscheduled shutdown and a cascade of damage to otherwise sound components.
The state-of-the-art is such that worst-case assumptions for engine operating parameters and its operating environment in conjunction with empirical structural and damage analysis techniques and practical operating experience are used to anticipate the life-limiting modes of damage accumulation and to predict deterministic safe operating life of the different turbine engine components and to fix a predetermined major time between overhaul (TBO) intervals for the engine. Major overhaul is by far the most expensive maintenance action item during the life cycle management of turbine engines. Any system that can provide realistic estimates of component lives would be an improvement on the current state-of-the-art.
Component level internal microstructural damage and distortion due to creep, LCF or TMF, is difficult to detect, and only empirical models are available to guess damage accumulation rates and the critical levels of damage beyond which remedial action would be essential. Current deterministic practices use operating times or numbers of operating cycles required to initiate detectable flaws in a large population of parts under worst case usage, and the statistical distribution of this data is used to determine the lower bound threshold for component replacement, typically −3 standard deviations from mean. This is adopted as the predetermined safe-life limit for all parts, regardless of the fact that the vast majority will contain no detectable flaws at this point, and hence have the capacity for further use. Furthermore, several analyses described in the literature have shown that 999 parts in a typical population of 1000 would, on average, have 10 to 20 lifetimes remaining at this point. This life can be harnessed using inspection based life cycle management of parts but the inspection intervals under creep, fatigue and combined loading conditions can only be developed using material physics based crack propagation modeling techniques.
Engine Parts Life Tracking Systems (EPLTS) have been developed to monitor life consumption and residual life of individual sets of components to schedule a TBO. In these systems, the engine usage is tracked and the speed and temperature data are stored and analyzed to isolate cyclic usage from steady state usage and to isolate mechanical cycling from thermal cycling. However, the life consumption and residual life of different components in EPLTS based systems are still computed using pre-determined safe life limits as opposed to actual usage based predicted life.
Condition based maintenance using diagnostics techniques for preventive maintenance have also been studied and these systems use relatively crude methods to monitor trends and major engine operating parameters such as temperatures and pressure ratios across different stages and fuel burn, from which gross changes in structural integrity may be inferred. Diagnostics based prognostics techniques, however, are only capable of picking gross faults and can be useful in preventing catastrophic failures but cannot be effectively used for residual life assessment purposes.
At present, there is no real-time prognostics system that has been developed for the predictive maintenance of multiple turbine engine components using physics based gas path modeling and loads and damage analysis techniques. Over the last three decades, tremendous advances have been made in improving the engine performance monitoring and data collection and trending capability. These systems typically use sensors and numerous advances have been made in monitoring systems to provide alarms and improve displays However, apart from predicting compressor fouling, the inability of performance monitoring systems to assist with predictive maintenance and TBO prediction remains unchanged. Extensive basic scientific research has indicated that component level failure is caused by usage-based loads that are responsible for the development of damage at the microstructural level. Therefore, continuous quantitative assessment of usage based thermal-mechanical loads and microstructural damage as a function of these service loads is vital for the development of any prognosis systems.
Following an exponential growth in the understanding and use of computational fluid dynamics techniques, the effect of thermal and aerodynamic loads on component level structural response has been extensively studied. While identification of thermal boundary conditions is important for gauging the component level structural response, the effect of underlying deformation and fracture mechanisms on life consumption and residual life has not received equal attention. Traditional research has focused on the computation of worst-case usage loads and the use of empirical damage modeling techniques to predict component level response to these worst-case thermal-mechanical loads. The use of empirical life prediction techniques also requires the generation of large but very expensive material databases along with a lot of field experience to accurately predict the future component behavior.
Evaluation of variability of component life as a result of variability in usage and microstructural features or stochastic material behavior has only recently come under investigation in turbine engineering and materials science respectively, and is generally not used in routine turbine engineering practice for the life cycle management of engines. Variability describes the degree to which gas path temperature profiles and component level usage loads change over time and also changes in microstructural features from one component to another and how these microstructural parameters also change over time during service. The initial distributions of microstructure often exist in a set of components and dynamics of some microstructural variables change over time during service and these distributions and their changes govern the material response to usage based thermal-mechanical loads during future service. A parameter such as the grain size may have an initial distribution in a set of turbine blades and vanes but may remain relatively constant during service, demonstrating a low degree of dynamic variability. Parameters such as intragranular precipitate size, grain boundary precipitate size and dislocation density may possess an initial distribution and their distributions may wildly change or shift with high variability during service. The initial as well as the dynamic variability of the microstructural features along with the variability in thermal-mechanical loads with time must all be considered for accurate life consumption and residual prediction.
The evaluation of inherent grain size variability has proven to contain valuable information regarding the creep behaviour of conventionally cast as well as forged components operating in high temperature and stress operating environments. It can provide accurate and reliable prognostic stratification of the risk of creep fracture in a population of components during service.
In addition, evaluation of grain boundary carbide variability due to primary carbide degeneration during service in cast turbine blades has revealed increased tendency for creep ductility reduction and material embrittlement. Similarly, variability in primary particulate distribution can influence the LCF life of parts.
Thus, initial and dynamic variability of microstructures in individual sets of components along with changes in in-service usage and operational conditions lead to variability in gas path temperatures and thermal-mechanical loads that control the life consumption and residual life. The significance of the evaluation of the effect of individual variables that influence life indicates that the continuous evaluation of multiple components will provide useful and accurate information on the TBO status of the engine. To date, there has been no attempt made to provide the engine users with the variability analysis of life consumption or residual life of multiple engine components on the basis of actual usage and usage based thermal-mechanical loads and material physics based damage analysis, nor provide the capability for continuous real-time variability analysis and display.